期刊
QUANTUM SCIENCE AND TECHNOLOGY
卷 4, 期 2, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/2058-9565/aaea94
关键词
quantum computing; machine learning; tensor networks
资金
- US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Quantum algorithm Teams Program [DE-AC02-05CH11231]
- NIH [S10OD023532]
Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks, that could already have benefits with such near-term devices. The result is a unified framework in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes in which, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. We demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a hybrid quantum-classical optimization procedure that could be carried out on quantum hardware today, and testing the noise resilience of the trained model.
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